#基于多层感知器的softmax多分类
from keras.models import Sequential
from keras.layers import Dense,Activation,Dropout
from keras.optimizers import SGD
import keras
#Generate dummy data
import numpy as np
x_train=np.random.random((1000,20))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test=np.random.random((1000,20))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model=Sequential()
model.add(Dense(64,activation='relu',input_dim=20))
model.add(Dropout(0.6))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.6))
model.add(Dense(10,activation='softmax'))

sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
model.fit(x_train,y_train,epochs=40,batch_size=128)

score=model.evaluate(x_test,y_test,batch_size=128)
print("accu_score:",score)



